ReviewBriefings in bioinformatics2016
Progress and challenges in bioinformatics approaches for enhancer identification.
Review in Briefings in bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 1 of them a synthesis that pooled it.
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Who cites it
52 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comprehensive multi-omics integration identifies differentially active enhancers during human brain development with clinical relevance.Genome medicine · 2021Pooled it
- UnlockingNAR genomics and bioinformatics · 2026Article
- DeepDualEnhancer: A Dual-Feature Input DNABert Based Deep Learning Method for Enhancer Recognition.International journal of molecular sciences · 2024Article
- Determination of key hub genes in Leishmaniasis as potential factors in diagnosis and treatment based on a bioinformatics study.Scientific reports · 2024Article
- Integration of chromosome locations and functional aspects of enhancers and topologically associating domains in knowledge graphs enables versatile queries about gene regulation.Nucleic acids research · 2024Article
- Predicting the Effect of miRNA on Gene Regulation to Foster Translational Multi-Omics Research-A Review on the Role of Super-Enhancers.Non-coding RNA · 2024Review
- Validated Negative Regions (VNRs) in the VISTA Database might be Truncated Forms of Bona Fide Enhancers.Advanced genetics (Hoboken, N.J.) · 2024Article
- Long extrachromosomal circular DNA identification by fusing sequence-derived features of physicochemical properties and nucleotide distribution patterns.Scientific reports · 2024Article
- PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies.Genome medicine · 2024Article
- Integrative approaches based on genomic techniques in the functional studies on enhancers.Briefings in bioinformatics · 2023Review
- Towards a comprehensive regulatory map of Mammalian Genomes.Research square · 2023Article
- Computational methods for identifying enhancer-promoter interactions.Quantitative biology (Beijing, China) · 2023Review
- Identification and characterization of enhancer elements controlling cell type-specific and signalling dependent chromatin programming during hematopoietic development.Stem cell investigation · 2023Review
- Genome-wide identification and characterization of DNA enhancers with a stacked multivariate fusion framework.PLoS computational biology · 2022Article
- Accurate prediction of functional states of cis-regulatory modules reveals common epigenetic rules in humans and mice.BMC biology · 2022Article
- Atlas of regulated target genes of transcription factors (ART-TF) in human ES cells.BMC bioinformatics · 2022Article
- Enhancer-LSTMAtt: A Bi-LSTM and Attention-Based Deep Learning Method for Enhancer Recognition.Biomolecules · 2022Article
- DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers.Nature genetics · 2022Article
- Review
- From shallow to deep: some lessons learned from application of machine learning for recognition of functional genomic elements in human genome.Human genomics · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Enhancers are cis-acting DNA elements that play critical roles in distal regulation of gene expression. Identifying enhancers is an important step for understanding distinct gene expression programs that may reflect normal and pathogenic cellular conditions. Experimental identification of enhancers is constrained by the set of conditions used in the experiment. This requires multiple experiments to identify enhancers, as they can be active under specific cellular conditions but not in different cell types/tissues or cellular states. This has opened prospects for computational prediction methods that can be used for high-throughput identification of putative enhancers to complement experimental approaches. Potential functions and properties of predicted enhancers have been catalogued and summarized in several enhancer-oriented databases. Because the current methods for the computational prediction of enhancers produce significantly different enhancer predictions, it will be beneficial for the research community to have an overview of the strategies and solutions developed in this field. In this review, we focus on the identification and analysis of enhancers by bioinformatics approaches. First, we describe a general framework for computational identification of enhancers, present relevant data types and discuss possible computational solutions. Next, we cover over 30 existing computational enhancer identification methods that were developed since 2000. Our review highlights advantages, limitations and potentials, while suggesting pragmatic guidelines for development of more efficient computational enhancer prediction methods. Finally, we discuss challenges and open problems of this topic, which require further consideration.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.